• Journal of Power Electronics
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• v.13 no.4
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• pp.584-591
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• 2013

In this paper, a hybrid booster power module with Si IGBT and Silicon Carbide (SiC) Schottky Barrier Diode (SBDs) is presented. The switching characteristics of the hybrid booster module are compared with commercial Silicon IGBT/Si PIN diode based modules. We applied the booster power module into a non-isolated on board vehicle charger with a simple buck-booster topology. The performances of the on-vehicle charger are analyzed and measured with different power modules. The test data is measured in the same system, at the same points of operation, using the conventional Si and hybrid Si/SiC power modules. The measured power conversion efficiency of the proposed on-vehicle charger is 96.4 % with the SiC SBD based hybrid booster module. The conversion efficiency gain of 1.4 % is realizable by replacing the Si-based booster module with the Si IGBT/SiC SBD hybrid boost module in the 6.6 kW on-vehicle chargers.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.6
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• pp.373-379
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• 2015

A hybrid navigation system is implemented to apply for a mobile robot. The hybrid navigation system consists of an inertial navigation system and a global positioning system. The inertial navigation system quickly calculates the position and the attitude of the robot by integrating directional accelerations, angular speed, and heading angle from a strap-down inertial measurement unit, but the results are available for a short time since it tends to diverge quickly. Global positioning system delivers position, heading angle, and traveling speed stably, but it has large deviation with slow update. Therefore, a hybrid navigation system uses the result from an inertial navigation system and corrects the result with the help of the global positioning system where an indirect feedback Kalman filter is used. We implement and confirm the performance of the hybrid navigation system through driving a car attaching it.

• Bulletin of the Korean Mathematical Society
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• v.50 no.1
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• pp.73-81
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• 2013

In this paper, an efficient hybrid nonlinear conjugate gradient method is proposed to solve general unconstrained optimization problems on the basis of CD method [2] and DY method [5], which possess the following property: the sufficient descent property holds without any line search. Under the Wolfe line search conditions, we proved the global convergence of the hybrid method for general nonconvex functions. The numerical results show that the hybrid method is especially efficient for the given test problems, and it can be widely used in scientific and engineering computation.

• Journal of the Korea Fashion and Costume Design Association
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• v.9 no.3
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• pp.167-180
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• 2007

This study analyzes the Asian, African and Latin American cultural hybrids in modem fashion, and offers a direction for desirable cultural hybrids in modem fashion. First, the cultural hybrids have been considered in two aspect: global hybrids and structural hybrids. Second, the trends of Asian, African and Latin American cultural hybrids have been interpreted differently depending on the cultural backgrounds of each area. However, the cultural hybrid representing the change of tradition in Asia, Africa and Latin America is a common trend, and is used to describe the social changes. Third, this study examines the global hybrid trend in modem fashion based on the hybrid trend of Asian, African and Latin American culture found in the four major collections from 2000 S/S to 2005 F/W. Until recently, the exotic images have been determined in the viewpoint of Western world, and utilization by the world-renowned designers in the four major collections plays the major role in converting the regional cultural elements into global ones. Fourth, this study also examines the structural hybrids in modem fashion based on the hybrid trend found in Asian, African and Latin American designer collections between 2000 S/S and 2005 F/W. The works which are connected to the world trend, but are also rooted from the cultural and regional traditions demonstrate the globalization of the Asian, African and Latin American fashion. Fashion is a messenger of a culture, and its importance as a symbol of a cultural trend is growing.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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• pp.38-46
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• 2003

Aim of this article is to propose Micro-Genetic Simulated Annealing (${\mu}GSA$) as a hybrid metaheuristics approach to find the global optimum of nonlinear optimisation problems. This approach combines the features of modern metaheuristics such as micro-genetic algorithm (${\mu}GAs$) and simulated annealing (SA) with the general robustness of parallel exploration and asymptotic convergence, respectively. Therefore, ${\mu}GSA$ approach can help in avoiding the premature convergence and can search for better global solution, because of its wide spread applicability, global perspective and inherent parallelism. For the superior performance of the ${\mu}GSA$, the five well-know benchmark test functions that were tested and compared with the two global optimisation approaches: scatter search (SS) and hybrid scatter genetic tabu (HSGT) approach. A practical application to structural sandwich panel is also examined by optimism the weight function. From the simulation results, it has been concluded that the proposed ${\mu}GSA$ approach is an effective optimisation tool for soloing continuous nonlinear global optimisation problems in suitable computational time frame.

• Structural Monitoring and Maintenance
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• v.1 no.4
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• pp.427-449
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• 2014

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

• Structural Engineering and Mechanics
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• v.42 no.6
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• pp.783-797
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• 2012

A new hybrid meta-heuristic optimization algorithm is presented for design of structures. The algorithm is based on the concepts of the charged system search (CSS) and the particle swarm optimization (PSO) algorithms. The CSS is inspired by the Coulomb and Gauss's laws of electrostatics in physics, the governing laws of motion from the Newtonian mechanics, and the PSO is based on the swarm intelligence and utilizes the information of the best fitness historically achieved by the particles (local best) and by the best among all the particles (global best). In the new hybrid algorithm, each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Three different types of structures are optimized as the numerical examples with the new algorithm. Comparison of the results of the hybrid algorithm with those of other meta-heuristic algorithms proves the robustness of the new algorithm.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• Journal of the Microelectronics and Packaging Society
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• v.25 no.4
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• pp.1-7
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• 2018

As the performance and density of IC (integrated circuit) devices increase, power and signal integrities in the global interconnects of advanced packaging technologies are becoming more difficult. Thus, the global interconnect technologies should be designed to accommodate increased input/output (I/O) counts, improved power grid network integrity, reduced RC delay, and improved electrical crosstalk stability. This requirement resulted in the fine-pitch interconnects with a low-k dielectric in 3D packaging or wafer level packaging structure. This paper reviews an organic-inorganic hybrid material as a potential dielectric candidate for the global interconnects. An organic-inorganic hybrid material called polysiloxane can provide spin process without high temperature curing, an excellent dielectric constant, and good mechanical properties.

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.33-41
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• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.